Object recognition is a cornerstone of the function of visual systems, whether neural or manmade. This capacity is indispensable for the function of a sensorimotor system operating in an external world, whether for navigation in complex terrain or rapid identification of the properties of food, friend, or foe. However, developing a manmade system to perform object recognition, which is so immediate and effortless for neural systems such as the mammalian brain, has proven toweringly difficult for conventional computing systems and software, despite decades of effort in computer vision and related fields of engineering. The best manmade systems for object recognition labor to deal with changes in the orientation and scale, and are defeated by the occlusion of one object with others which ubiquitously occurs in environments. Such performance is intolerable in any real-world application, whether military or commercial.